Content Feature Based Image Retrieval And Integrated Video Retrieval

Efficient content-based image retrieval has developed tremendously in many application areas. Content-based image retrieval research areas had established many systems, but these systems have deficiency for actually usage:(1) these systems are expected to process different kinds of image retrieval by same method and (2) these systems are designed without considering user requirement. In fact, different kinds of image retrieval need different retrieval mode. So it presents image interactive retrieval based-on integrated region similarity match. This system is an image retrieval system with interaction and semantic classification of based-on wavelet feature extraction and image integrated region similarity method. Compared with other retrieval methods of based-on image content, the method enhances retrieval efficiency by permitting adaptive search and interaction and narrowing search area. Experiment result shows that the system is more precise and efficient than other retrieval systems and more robustly of variety image.In this paper, it also propose a new method of search engine of image compare, it establishes image feature database by new fractal image processing and index. By image fractal code and obtaining its iterative function, the image and iterative function are saved database becoming index file of the image. When database is searched and index file is compared so that user retrieval images similar to query image. Based on fractal coding the index file have two characters:First, similar images have similar iterative function so as to produce similar index file, and the similar index file can retrieval the similar image; Second, unsimilar images have unsimilar iterative function, vice versa. The fractal coding produces large of datum and need an effective method to retrieval. So this paper based on Fisher discriminant function estimating images similarity, and determine similarity queue of all images in database and query image. Experiment shows that image compare search engine besides can get exactly similar image of query image, it can get right feedback image for query image with rotation, faintness, noise and so on. This method has been testified better to tolerate-error of image rotation, faintness, noise and so on, it can effectively improve adaption of image compare search engine.As to video content-based video index techniques, besides providing correct and quick response index result, a good video index should also provide content retrieval of correlation similarity. That is to say, the system must be able to establish a set of dusting classification of similarity, so that users can also query correlation content. This kind of flexible query can avoid problems caused by the traditional SQL query system. In fact, users always are not aware of what they wantto query, even if they know, it is difficult to discribe and definite the content effectively. Therefore, browse function and similarity query can help users query more flexibly.According to what mentioned above, it proposes a new index approach, aiming to provide both accurate query results and flexible query of similar content. The new index system is a new index technique combining fuzzy clustering classification theory and a new algorithm. Result shows that this new index technique can query correctly and quickly.